JMIR Medical Education

Social media platforms are increasingly integrated into higher education, enabling collaborative, student-centered learning. Yet, few instruments specifically measure students’ satisfaction with these activities across platforms. A brief, valid tool is needed to evaluate perceived quality and guide instructional design in social media–based learning environments.

The rapid transformation of the healthcare landscape requires physicians to not only be skilled clinically but also navigate and lead a highly dynamic, innovation-driven environment. This also provides an avenue for physicians to significantly enhance their ability to help their patients, through participation in health innovation projects. Despite this growing need and opportunity, few medical schools provide formal training in innovation and entrepreneurship (I&E). In this perspective, we examine the need for I&E education in medical curricula by exploring student interest, effective program models, and implementation strategies. To better understand medical student interest in innovation and willingness to participate in I&E programs during medical school, we surveyed 480 medical students at our institution, the Johns Hopkins University School of Medicine (19% response rate). We observed a strong interest in healthcare I&E, with 97% (n = 87) of respondents valuing knowledge or experience in I&E and 63% (n = 56) expressing intent to incorporate I&E into their careers. To assess the real-world impact of I&E education on medical professionals, we surveyed 12 alumni of the Johns Hopkins Center for Bioengineering Innovation and Design (CBID) Master’s program who had also completed medical school. Graduates reported that their experiences cultivated transferable skills—design thinking, interdisciplinary collaboration, and leadership—that shaped their professional trajectories. We propose three models for incorporating I&E education into existing medical curricula—short-term workshops, one-year gap programs, and longitudinal tracks—and discuss their advantages and tradeoffs. Early and structured exposure to I&E education in medical school empowers students to identify unmet clinical needs, collaborate across disciplines, and develop real-world solutions. As the pace of innovation continues to accelerate, integration of I&E education into medical curricula offers a timely opportunity for medical schools to cultivate physician leaders in this space.

Electronic Medical Records (EMR) are a potentially rich source of information on an individual healthcare providers’ clinical activities. These data provide an opportunity to tailor online learning for healthcare providers to align closely with their practice. There is increasing interest in the use of EMR data to understand performance and support continuous and targeted education for healthcare providers.

The evolution of the healthcare landscape necessitates expanding the roles of pharmacists in patient-centered care to encompass direct patient management, collaborative practice, and preventive service. These responsibilities can be fulfilled by pharmacists through ongoing professional development, in which continuing education (CE) is instrumental to career advancement and improved patient care.

Artificial intelligence (AI) is rapidly reshaping medical education, offering new opportunities to personalize learning, enhance research, and streamline administration. The aim of this study is to provide 12 practical, evidence-informed tips by drawing on current literature and real-world examples to guide the integration of AI into medical education, supporting educators across teaching, research, administration, and ethical domains. Key strategies include using adaptive learning platforms to tailor educational content, using AI tools to provide timely feedback, and incorporating AI-generated clinical scenarios in case-based learning. The importance of fostering AI literacy among students is emphasized, as well as utilizing AI-powered tools for efficient literature reviews, data analysis, and manuscript preparation. Administrative applications such as automating routine tasks, supporting strategic planning through data analysis, and enhancing faculty development with AI-driven platforms are also discussed. Ethical considerations are highlighted, with a focus on ensuring transparency, fairness, and accountability in all AI applications. By following these 12 tips, medical educators can leverage the benefits of AI to improve educational outcomes, increase efficiency, and prepare future clinicians for a technology-driven health care environment.

Case-based learning using standardized patients is a key method for teaching communication skills in medicine. Besides logistical and financial hurdles, standardized patients portrayed by actors cannot cover the complete diversity of sociodemographic factors of patients. Large language models (LLMs) show promise for creating scalable patient simulations and could probably cover a broader diversity of factors. They could also be integrated into the continuous training of future health care professionals’ communication and interaction skills.

Clinicians are central to treating tobacco use disorder, yet practical training is inconsistent, and confidence varies. Brief, text message–based microlearning may offer a low-burden way to strengthen foundational competencies in busy clinical settings.

The optimal duration of emergency medicine (EM) residency training remains a subject of national debate, with the Accreditation Council for Graduate Medical Education considering standardizing all programs to four years. However, empirical data on how residents accumulate clinical exposure over time are limited. Traditional measures, such as case logs and diagnostic codes, often fail to capture the breadth and depth of diagnostic reasoning. Natural language processing (NLP) of clinical documentation offers a novel approach to quantify clinical experiences more comprehensively.

Clinical internal medicine practice training traditionally relies on case-based teaching. This approach limits the development of students' clinical thinking skills. It also places significant pressure on instructors. Virtual Standardized Patients (VSPs) could offer an alternative solution. However, evidence on their feasibility and effectiveness remains limited.

Early exposure to research methodology is essential in medical education, yet many students show limited motivation to engage with non-clinical content. Gamified strategies such as educational escape rooms (EERs) may help improve engagement, but few studies have explored their feasibility at scale or evaluated their impact beyond student satisfaction.

Medical education continues to favor didactic lectures as the predominant method of instruction. However, in recent years, there has been a shift to active learning methodologies, such as gamification.

Artificial intelligence (AI) has the potential to transform training through adaptive learning, immersive simulations, automated assessments, and data driven insights, offering solutions to persistent issues such as high student to faculty ratios, overcrowded classrooms, and limited clinical exposure. Globally, many universities have already embedded AI literacy and competencies into undergraduate, postgraduate, and continuing education programs, while in Vietnam the use of AI in medical education remains limited and fragmented. This Viewpoint aims to assess the opportunities and challenges of using AI in medical education in this country. Most students have little formal exposure to AI, and empirical evidence on faculty or institutional readiness is scarce. Experiences from other countries including Malaysia, Palestine, and Oman demonstrate that incremental adoption and faculty development can facilitate cultural acceptance and curricular innovation, providing useful lessons for Vietnam. At the same time, significant barriers remain. These include inadequate infrastructure in provincial universities, low levels of AI literacy among both students and educators, underdeveloped regulatory and ethical frameworks, and resistance to pedagogical change. Cost effectiveness and sustainability are additional concerns in a middle-income context, where upfront investments must be balanced against long term benefits and equitable access. Advancing AI in Vietnamese medical education will therefore require a coordinated national strategy that prioritizes infrastructure, AI literacy, faculty development, quality assurance, and sustainable funding models, alongside ethical and legal safeguards. By addressing these foundations, Vietnam can harness AI not only to modernize medical education but also to strengthen preparedness for a digitally enabled health workforce.